Three-dimensional motion capture

ABSTRACT

In one general aspect, a method is described. The method includes generating a positional relationship between one or more support structures having at least one motion capture mark and at least one virtual structure corresponding to geometry of an object to be tracked and positioning the support structures on the object to be tracked. The support structures has sufficient rigidity that, if there are multiple marks, the marks on each support structure maintain substantially fixed distances from each other in response to movement by the object. The method also includes determining an effective quantity of ray traces between one or more camera views and one or more marks on the support structures, and estimating an orientation of the virtual structure by aligning the determined effective quantity of ray traces with a known configuration of marks on the support structures.

This application claims benefit from Provisional Application No.60/662,973 filed on Mar. 16, 2005 “Apparatus and Method of Capturing theMotion of Humans, Creatures, or Objects,” and incorporated herein byreference.

TECHNICAL FIELD

This document relates to a motion capture system and method.

BACKGROUND

Motion capture is an approach to generating motion data that is based ontracking and recording the movement of real objects. One commonapplication of motion capture is in animation where a realistic sequenceof motion, e.g., by a human actor, can be captured and used to representthe motion of an animated object.

In some motion capture systems, an actor wears a black body suit. Anumber of white balls are attached to the suit at the actor's joints,e.g., shoulder, elbow, wrist. The actor then performs a sequence ofmovements which is digitally recorded by a number of cameras. Therecorded data is then processed by a motion capture program.

The motion capture program recognizes the white balls as points. Ifmultiple cameras record the same point in a frame and the locations ofthe cameras are known, the motion capture program can determine the 3Dposition of the point using triangulation. The motion capture system maydetermine 3D positions for all of the points in each of the frames. Asused herein, the term “frame” refers to a period of time, or acollection of different periods of time, at which a 3D position of anobject being captured is calculated.

The 3D points may be input into a fitting program that fits a virtualskeleton, which represents the bone structure of the object beingcaptured by the system, to the 3D points. For example, an upper arm bonecan be defined to exist between a point associated with a shoulder and apoint associated with the elbow. The motion capture program may trackthe movement of the points from frame to frame, which, once fitted tothe virtual skeleton, defines the movement of the skeleton from frame toframe.

In some systems, however, use of triangulation may require a relativelylarge number of cameras to capture each of the differently positionedpoints from multiple camera angles. Although only two cameras may berequired to triangulate a single point, the two cameras typically needto track the point during each frame. As a result, if only two camerasare used, the cameras can lose track of a white ball as a result of theactor turning around. Thus, when an actor performs a scene with lots ofmotion, a large number of cameras, e.g., 20, are typically utilized toensure that at least two cameras can track each white ball during eachframe.

In some motion capture systems, it may be difficult to determine whichwhite ball recorded with a first camera at a first angle correspondswith the same white ball taken from a second camera at a second angle.When the white balls recorded by two cameras are not correctlyassociated, the fitting program may produce some artifacts in the formof physically impossible motions in an attempt to fit the skeleton tothe 3D points. For example, a leg can suddenly appear to move 180° fromone frame to the next frame. While dramatic changes can be easy to spot,subtle mistakes may be difficult to catch.

Additionally, some motion capture systems tend to require refinedlighting conditions. For example, the white balls can be formed asobjects that reflect infrared (IR) light, and the cameras may be tunedto respond to IR light. When an IR light source is directed toward thewhite balls, the cameras pick up the points of IR light reflected backfrom the actor. This approach, however, may not work well on atraditional movie set because when the lights are turned up on a movieset they become quite hot and unintentionally flood the scene with IRlight. As a result, the cameras may not be able to distinguish a pointof reflected IR light from background noise.

SUMMARY

The present document relates to a system and method for 3D motioncapture.

In one general aspect, a method is described. The method includesgenerating a positional relationship between one or more supportstructures having at least one motion capture mark and at least onevirtual structure corresponding to geometry of an object to be trackedand positioning the support structures on the object to be tracked. Thesupport structures has sufficient rigidity that, if there are multiplemarks, the marks on each support structure maintain substantially fixeddistances from each other in response to movement by the object. Themethod also includes determining an effective quantity of ray tracesbetween one or more camera views and one or more marks on the supportstructures, and estimating an orientation of the virtual structure byaligning the determined effective quantity of ray traces with a knownconfiguration of marks on the support structures.

In second general aspect, a system is described. The system includes aninterface to receive recordings from one or more cameras. The recordingsinclude image data of one or more marks on one or more rigid orsemi-rigid support structures positioned on an object. The supportstructures have a substantially fixed position relative to virtualstructures generated for the object. The system also includes a raytracer to generate an effective quantity of ray traces between one ormore camera views from the cameras and the marks, and an orientationestimator to estimate orientations of the virtual structures by aligningthe effective quantity of ray traces with a known configuration of markson the support structures.

In another general aspect, an apparatus is described. The apparatusincludes a support structure configured to be affixed to an actor whosemotion is to be tracked using one or more motion capture cameras, thesupport structure having a plurality of motion capture marks andsufficient rigidity that, in response to movement by the actor, themarks on the support structure maintain substantially fixed distancesfrom each other.

In yet another general aspect, a motion capture system is described. Themotion capture system includes at least one support structure configuredto be affixed to an actor whose motion is to be tracked, the supportstructure having a plurality of motion capture marks and sufficientrigidity that, in response to movement by the actor, the marks on thesupport structure maintain substantially fixed distances from eachother, a motion model comprising one or more virtual structuresrepresenting geometry of the actor to be tracked, and a computer system.The computer system is capable of executing a plurality of substantiallysimultaneous processes, including one or more processes to perform thefollowing: (a) generate a positional relationship between the at leastone support structure and the one or more virtual structuresrepresenting geometry of the actor; (b) determine an effective quantityof ray traces between one or more camera views and one or more marks onthe support structures; and (c) estimate orientations of the virtualstructures by aligning the effective quantity of ray traces with a knownconfiguration of marks on the support structures.

The systems and techniques described here may provide one or more of thefollowing advantages. First, accurate motion capture may be accomplishedusing a decreased number of cameras, which may reduce the cost andcomplexity of the system. Second, an object's motion may be captured ina variety of lighting conditions, which may include substantiallycomplete darkness. Third, a three dimensional position of a mark on anobject may be calculated even though the mark is only captured by onecamera or is not captured at all. Fourth, a system may include an objectmodel that facilitates prediction of motion based on a unique object'stypical range of motion and physical structure.

The details of one or more embodiments are set forth in the accompanyingdrawings and the description below. Other features and advantages of thedescribed embodiments will be apparent from the description anddrawings, and from the claims.

DESCRIPTION OF DRAWINGS

FIG. 1 is a diagram illustrating an example of a motion capture system.

FIG. 2 is a flow chart illustrating an example of a method forprocessing frames of motion capture data.

FIG. 3 is a flow chart illustrating an example of a method forcalculating a 3D position of each visible mark on each support structurein a frame.

FIG. 4 is a flow chart illustrating an example of a method for trackingeach visible mark from a first frame to a second frame.

FIG. 5 is a block diagram illustrating an example of a computer.

Like reference symbols in the various drawings indicate like elements.

DETAILED DESCRIPTION

In a system 100 of FIG. 1, a number of marks, which may be detected byone or more camera(s), are placed on rigid or semi-rigid supportstructures. The support structures, in turn, are attached to anunderlying object, such as an actor 114 or a body suit of the actor. Asused herein, a rigid structure is one in which the movement of the markson a same support structure is negligible relative to the marks'positions from each other. Additionally, a semi-rigid structure is onein which the movement of the marks on a same support structure ispermitted, but the movement is substantially limited within apredetermined range. The amount of the movement between the marks may bebased on several factors, such as the type of material used in thesupport structure and the amount of force applied to the supportstructure. For example, a flexible body suit, depending on materialsused and methods of construction, may qualify as a “rigid” or“semi-rigid” support structure in the context of the disclosedtechniques provided that it demonstrates the appropriate level ofrigidity. Additionally, bands attached to a body suit may also qualifyas the rigid or semi-rigid support structure.

In some embodiments, the mark-to-mark spacing on a support structure maydeterminable in techniques discussed in more detail below, and thus thespacing does not need to be known a-priori. The system can use one ormore cameras to track different marks on the support structures. Thesemarks may be used to estimate the motion (e.g., position and orientationin 3d space through time) of the support structures. The knowledge thateach support structure is rigid (or semi-rigid) may be used in theestimation process discussed below and may facilitate reconstruction ofthe motion from a single camera.

In some embodiments, the marks can made of high-contrast materials, andmay also optionally be lit with light emitting diodes (LEDs) orluminescent materials that are visible in the dark. These lightingqualities can enable cameras to capture the marks on the object in lowlighting or substantially dark conditions. For example, an actor beingfilmed may walk from a well lit area to a shadowed area. The marks maybe captured despite the actor's movement into the shadowed area becausethe marks glow or emit light.

In operation, one or more video cameras can record a motion sequence,and store the images to a storage device, such as a tape or disk, forprocessing. Also, the images may be directly transmitted to a processingdevice for processing in real-time or near real-time.

An operator or algorithm computes the location of the camera and itsvisual properties, such as the camera's field of view, lens distortion,and orientation, while the sequence was being recorded. For example, analgorithm can derive the position of the camera given enough marks andinformation associated with the marks, such as the number,identification, and position of the marks captured by that camera.

FIG. 2 shows a flow chart that illustrates an example of a method 200for processing frames of motion capture data to compute a relationshipof the marks on a structure to the underlying object being tracked. Inthe embodiment shown in FIG. 2, each of the support structures isassociated with a virtual structure, such as a virtual bone structuregenerated to model a skeleton of the object, in step 202. For example,the support structure may be a band that surrounds an actor's arm. Theband can be ring shaped and occupies a 3D space defined by X, Y, and Zaxes. The point in the object space of the ring where the values on theX, Y, and Z axes meet (e.g., X=Y=Z=0) is the geometric center of theband. In some embodiments, this geometric center may be substantiallyaligned with a geometric center of a virtual arm bone. In otherembodiments, the geometric center of the virtual arm bone may be offsetrelative to the geometric center of the ring.

The cameras capture, or record, marks located on the support structuresand the recordings are used to identify locations where the marksappear, as shown in step 204. An algorithm may calculate one or more raytraces extending from one or more cameras through one or more of therecorded marks, as shown in step 206. For example, algorithm maycalculate two ray traces. Both ray traces may extend from a singlecamera view to a first recorded mark and a second recorded mark,respectively.

Information about the support structure, such the distance between themarks on the structure, the rigidity of the structure, and the geometryof the structure may be generated by the system (discussed in greaterdetail below) or input by a user. This information may define aconfiguration describing where the marks on a structure should or couldoccur. For illustrative purposes the configuration information isdescribed herein as a virtual support structure in 3D space.

An algorithm can estimate the current 3D orientation of the virtual bonestructures by aligning the virtual support structure with the 3D raytraces, as shown in step 210. This alignment may be implemented usingseveral different types of solving algorithms, such as a maximumlikelihood estimation or a Levenberg-Marquardt nonlinear minimization ofa heuristic error function. The algorithm's solution will move the 3Dposition of the marks on the virtual support structures to lie on the 3Dray traces. After alignment, the current orientation of the virtual bonestructures is known because the virtual support structure has asubstantially fixed relationship to the orientation of its correspondingvirtual bone structure.

In some embodiments, two or more cameras may record multipleobservations of the same mark. The alignment algorithm may use everyadditional recording of a mark's position as an additional constraint inthe solving calculation. If no marks on a support structure are capturedby a camera, observations of marks on other support structures can beused to estimate the position of the uncaptured support structure, or atleast constrain it to an area of space. For example, a support structurearound and actor's elbow may not be visible, however, a supportstructure around the actor's wrist may be visible. The position of avirtual wrist bone corresponding to the wrist support structure may becalculated in the manner described above. Because the position of thevirtual wrist bone is known, the possible locations of a virtual elbowbone corresponding to the elbow support structure are constrained to alimited area. This constraint may be based on an object motion model,which can substantially define the physical structure of the actorincluding a length of the actor's bone that connects the elbow to thewrist. The object motion model is discussed in more detail below.

Given these estimates or bounds for the 3D positions of the virtual bonestructure, the motion of the underlying object can be estimated. Incases where the a position of a marks cannot be used to estimate themotion (e.g. some parts are not observed by any camera), one or morephysical properties of the object, such as the natural limits of therange of motion for an actor's leg, can be used to infer the most likelyposition of the mark (and thus the motion of the object) based oncurrent observation of virtual bone structures with known 3D positionsand estimates derived from the placement of virtual bone structures atprior and future periods in time.

Optionally, an object motion model may be accessed, as shown in step208, to further constrain the algorithm used to solve for the alignmentof the ray traces with a known configuration of the marks. An objectmotion model can substantially define the motion of the underlyingobject. The mass and articulation properties of an object, the range ofmotion, velocity, and accelerations of a particular human's physique canbe derived based on previously captured motion information for theobject. For example, several cameras and a traditional motion capturedevice may capture an actor's bone lengths and typical range of movementby recording the actor's motions and triangulating the position of whiteballs placed on the actor. The captured motion may be then be used toderive a model specific to that actor.

After one frame of captured image data is processed, the method 200 candetermine whether there are more frames of data to process, as shown instep 212. If there are more frames, the method can return to step 202.Otherwise, the method may end.

FIG. 1 shows a diagram that illustrates an example of a motion capturesystem 100. As shown in the FIG. 1 example, system 100 includes a numberof support structures 110, and a number of marks 112 that are attachedto each support structure 110.

In the present example, the support structures 110 are implemented ascylindrical bands that are wrapped around an underlying object, such asan actor 114. In some embodiments, the support structures 110 are rigidand cannot be flexed to a significant extent (e.g., relative toaccuracy/precision of the cameras and/or motion capture program).Additionally, the support structures 110 may be semi-rigid and can beflexed in a limited manner.

Further, each of the support structures 110 can have any of a number ofcolors, such as a series of colors that sharply contrast with eachother. In the FIG. 1 example, each of the support structures 110 has aseries of alternating black and white square regions.

The marks 112, in turn, are attached to a support structure 110 so thata pair of adjacent marks 112 may be separated by a fixed or semi-fixeddistance from each other when measured along an axis of the supportstructure 110 that passes through the marks. Each pair of adjacent marks112, in turn, can be separated by the same distance. For example, anumber of marks 112 can be formed on a support structure 110 so that themarks 112 are evenly spaced apart. In some embodiments, the marks do notneed to be evenly spaced apart, but still may have a distance from othermarks that does not substantially change.

When a rigid support structure 110 is utilized, the distance between anadjacent pair of marks 112 can be substantially fixed. As a result, thedistance remains substantially unchanged when the underlying object 114moves from one position to another position.

When a semi-rigid support structure 110 is utilized, the distancebetween an adjacent pair of marks 112 may not be fixed but rather mayvary to a limited extent. As a result, the distance can vary within arange when the underlying object 114 moves from one position to anotherposition. For example, the distance between an adjacent pair of marks112 on a semi-rigid support structure 110 can vary because the structure110 may flex or twist. In some embodiments, the amount of possiblevariation due to one or more forces applied to the support structure ismeasured and input into the system 100 to compensate for possiblechanges in observed distances between marks. Thus, a semi-rigid supportmember may restrict the movement of the marks 112 to a limited range ofpositions depending on variables, such as the material of the supportmember and the direction and magnitude of a force applied to the member.

In addition, the marks 112 can have any of one or more geometric shapes.For example, the marks 112 can be implemented with circles, triangles,squares, or rectangles. As shown in FIG. 1 example, each mark 112 hasthe same geometric shape, and is implemented with a dot.

In addition to having one or more shapes, the marks 112 can have any ofone or more colors, such as colors that sharply contrast (e.g., acontrast ratio of 400:1) with the colors of the support structure 110.In FIG. 1 example, the marks 112 have a series of alternatingcontrasting colors (white and black) that are opposite to the series ofalternating contrasting colors (black and white) on the supportstructure 110.

In addition, in some embodiments, the marks 112 can be implemented witha number of contrasting (e.g., black and white) lines. One advantage ofusing marks 112 with a number of contrasting lines is that the lines canbe used to form a bar code which, in turn, can be used to uniquelyidentify the mark 112. For example, the computer 124 may include anindex that correlates observed bar codes with particular marks. When abar code is captured by a camera, the index may be access to identifywhich mark is specified by the code (e.g., the identified mark may be amark placed at the inner side of the left elbow on an elbow supportstructure).

Further, the marks 112 can be covered with or composed of a luminousmaterial, or can be self-illuminating, such as marks 112 thatincorporate light emitting diodes (LEDs). For example, infra-red,self-illuminating marks 112 can be seen when it is substantially totallydark, and, as a result, provide invariance to lighting conditions.

Thus, the luminous or illuminated marks may facilitate performing motioncapture of a scene at night or, more commonly, when an actor is walkingout of a dark shadow into light; or the actor stands in a dark areaamong a number of characters, and then steps out into a light area.

The marks 112 can be planar with respect to the support structure 110,can be non-planar (e.g., protrude) with respect to the support structure110, or a combination thereof. In the FIG. 1 example, each mark 112 isimplemented with a dot that lies in the same plane as the supportstructure 110. Further, individual marks 116 which are unrelated to asupport structure 110 can be used.

In addition to the support structures 110 and marks 112, system 100 alsoincludes a one or more cameras 120 that capture images of the motion ofthe marks 112 as the actor 114 performs a series of movements. Further,system 100 includes a storage medium 122 that is connected to thecameras 120 to digitally record the images captured by the cameras 120.

System 100 also includes a computer 124 that is connected to storagemedium 122. As described in greater detail below, computer 124 canexecute a motion capture program that tracks the movement of the marks112 from frame-to-frame to extract the motion of an underlying object,such as an actor.

In operation, one or more cameras 120 are placed around actor 114. Actor114, in turn, wears a black body suit, while a number of supportstructures 110 are placed around the body of actor 114 to recover theskeletal motion of actor 114. Additionally, individual marks 116 can beplaced directly on the actor's chest and back. (A support structure 110can alternately—or in addition—be placed around the actor's chest.)

Any number of marks 112 located on the support structure 110, includingzero, can be seen by each camera. For example, a support structure 110can have three marks 112 that are fully visible from each camera view.In another example, only a single mark 112 can be seen in a camera view.In some embodiments, none of the cameras 120 need to see the same marks112 to reconstruct the motion of the actor's virtual bone structure.

Once system 100 is set up, actor 114 can perform a series of movements.The support structures 110 allow the marks 112 to have a substantiallyfixed relationship with actor 114 and to each other. In someembodiments, when actor 114 moves, the marks 112 follow the movementsubstantially as though the marks 112 were rigidly attached to points onactor 114.

The movements of the marks 112 on the support structures 110 are thencaptured by the cameras 120, and digitally recorded in storage medium122. The recorded data then may be processed by computer 124 using amotion capture program.

As discussed earlier, FIG. 2 shows a flow chart that illustrates anexample of the method 200 for processing frames of motion capture datato derive the orientation of an object's virtual bone structure.Combining the processed frames generates information associated with thetracked movement of the object's bone structure from frame to frame.

FIG. 3 shows a flow chart that illustrates an example of a method 300 ofcalculating the position of each virtual structure associated with eachsupport structure in a frame. As shown in FIG. 3, a method 300 begins in310 where a camera view is selected. The camera view has a 3D positionin space which is either known by the motion capture program ordetermined based the position of marks recorded by the camera andinformation associated with the marks as discussed above.

Once a camera view has been selected, a support structure is selectedfrom the camera view in 312 by selecting the visible marks thatrepresent the support structure in the camera view. In 314, ray tracesare extended from the camera view through the marks which are visible inthe camera view.

For example, if two marks on a support structure can be seen in a cameraview (which is determined by the camera's properties, such as position,field of view, and orientation), then a first ray is extended from thecamera view through the first mark, while a second ray is extended fromthe camera view through the second mark. Once the rays have beenextended, geometric, or perspective, projection data for each ray isdetermined and recorded in 316.

Following this, method 300 determines in 318 if additional supportstructures in the camera view remain to be considered. For example,additional support structures may remain if any marks have not beenprocessed yet. If additional support structures remain to be considered,method 300 returns to 312 to select another support structure from thecamera view by selecting the visible marks that represent the supportstructure in the camera view. This process continues until all of thesupport structures in the camera view have been considered.

When no more support structures in the camera view remain to beconsidered, method 300 moves to 320 to determine if additional cameraviews remain to be considered in the frame. If additional camera viewsremain to be considered, method 300 returns to 310 to select anothercamera view. This process continues until all of the camera views in theframe have been considered.

When no more camera views in the frame remain to be considered, method300 moves to 322 to calculate the 3D positions of the virtual bonestructure as described in association with FIG. 2. Additionally, in someembodiments, the 3D positions of the bone structure associated with asupport structure can be determined using conventional geometric solversthat utilize as inputs the geometric projection data of the rays, the 3Dpositions of the camera views, the geometry of the support structure,and the fixed spacing relationship that exists between the marks on thesupport structure.

Returning to FIG. 2, the 3D position of a support structure, and thus avirtual structure, can be calculated from the positions of the visiblemarks on the support structure.

For example, the 3D position of a rigid support structure can bedetermined if three rays pass through three different visible marks. Inthis case, the 3D positions of the three different visible marks can bedetermined due to the fixed spacing relationship that exists between themarks.

Since the 3D positions of three different marks are known, the 3Dposition of the support structure is fixed in space and can therefore becalculated. Further, the 3D position of each remaining (non-visible)mark on the support structure can also be determined based on thesubstantially fixed spacing relationship that exists between the marks.

In some embodiments, the 3D position of a rigid support structure maynot be determined if only two rays pass through two different visiblemarks. In this case, the 3D positions of the two different visible markscan be determined due to the fixed spacing relationship, but the twodifferent visible marks do not allow the position of the supportstructure to be fixed in space.

However, an estimate of the 3D position of the support structure can becalculated because the 3D positions of the two different marks bound thesupport structure to lie within a limited range of 3D positions. Toincrease the accuracy of the estimation, past motion data, which furtherlimits or constrains the 3D position of the support structure, can alsobe used.

In some embodiments, if one ray passes through a single mark that isassociated with a rigid support structure, there may be insufficientdata to determine the 3D position of the single mark. However, whenutilized with motion models and position data of other supportstructures, estimates of the 3D position can be made.

In some situations, the 3D position of a semi-rigid support structurecannot be determined if three rays pass through three different visiblemarks, however, an estimate of the 3D position can be made. In thiscase, both the fixed spacing relationship that exists between the marksis known, and the stiffness, or rigidity, of the support structure isknown. As a result, the maximum amount of flex, compression, and stretchthat can occur between adjacent marks is limited and can be modeled.

As before, less information may be available when only two rays passthrough two different visible marks on a semi-rigid support structure,or one ray passes through one visible mark a semi-rigid supportstructure. However, as before, estimates can be made of the 3D locationof the support structure in both of these cases.

With respect to the substantially fixed spacing relationship that existsbetween the marks on the support structure, the spacing relationship canbe known a-priori, unknown a-priori, or a combination thereof. If themark-to-mark spacing is not known ahead of time, the spacing can beestimated during processing using multiple recordings of the marks andinformation associated with the marks because the mark-to-mark spacingis substantially fixed.

The system 100 can use the above described methods to determine the 3Dposition of the marks instead of using triangulation, which may dependupon two cameras recording the same mark at the same time. The abovedescribed methods may decrease the number of cameras required todetermine a 3D position of a mark because the determination may onlyrequire one camera to record the mark.

Referring again to FIG. 2, when the virtual structure is a skeleton,method 200 calculates the 3D position of the skeleton in the first frameby calculating the 3D position of each bone in the first frame. The 3Dposition of a bone in the first frame is calculated using the 3Dpositions of the support structures associated with the bone, as shownin step 210, along with knowledge of the length and orientation of thebone with respect to the support structures, which may be accessed asshown in optional step 208.

For example, if the 3D position of a rigid support structure formedaround an elbow has been calculated, and the 3D position of a rigidsupport structure formed around a wrist has been calculated, then the 3Dposition of the bone that lies between the elbow and the wrist can alsobe calculated using only the orientation of the bone with respect to theelbow and wrist support structures.

In other embodiments, when an estimate of the 3D position of a rigid orsemi-rigid support structure formed around an elbow has been calculated,and an estimate of the 3D position of a rigid or semi-rigid supportstructure formed around a wrist has been calculated, then knowledge ofthe length and orientation of the bone with respect to the elbow andwrist support structures may allow an estimate of the 3D position of thebone that lies between the elbow and the wrist to be calculated.

Once a 3D position of the virtual structure in the first frame has beencalculated, method 200 repeats to calculate a 3D position of the virtualstructure in a second frame. To calculate the 3D position of a supportstructure in the second frame, the 3D positions of the virtual structurecan be tracked from the first frame to the second frame. Thus, method200 can track the movement of the virtual structure from frame to framewhich, when associated with a virtual skeleton, can define the movementof the skeleton from frame to frame.

FIG. 4 shows a flow chart that illustrates an example of a method 400 oftracking each visible mark from a first frame to a second frame. Asshown in FIG. 4, method 400 begins in 410 where a visible mark isselected as a selected mark. Next, in 412, a prior frame to a firstframe motion vector for the selected mark in the first frame iscalculated.

Following this, in 414, a position of the selected mark in the secondframe is estimated based on the motion vector associated with the mark.Optionally, method 400 can also use motion model data to calculate anestimate, or verify the estimate. For example, if motion model studiesindicate that an arm cannot move any further in a particular direction,then the estimate can be adjusted to ignore contradictory data and/oradjust the solution globally to best fit the known limits.

A motion model can describe how each of the joints of an actor can movecorrelated with other joints. For example, if the actor is standing in aposition and an arm is moving up, chances are the arm in the next frameis going to be in one of a limited number of positions. As a result, alimit can be put on the range of relative movement between joints and/orlimbs.

In 416, a region in space associated with the selected mark is searchedto find the selected mark in the second frame, based on the estimatedposition associated with the mark, to identify the selected mark in thesecond frame. When the position and motion vector of a mark is known,and any motion model limits are known, then the location of the mark inthe second frame can be estimated. As a result, the search can belimited to an area where the mark is expected to be located.

In addition, if another actor in the next frame walks in front of themarks on a support structure so that nothing is seen, then theobservations can be thrown out. In this case, the position and motionvector of the marks on a support structure, along with motion model datawhich constrains the movement (e.g., an arm cannot whip from one extremeto another extreme in a single time frame), can be used to generate anestimate of the 3D position of the occluded mark in the next frame.

Method 400 is performed for each visible mark on each support structurein the first frame. Once each visible mark in the first frame has beentracked and identified in the second frame, the 3D position of eachsupport structure can be calculated in the second frame in the samemanner as described above.

Once a 3D position of the virtual structure in the second frame has beencalculated, the motion capture system may determine if the movement ofthe virtual structure (e.g., the skeleton) from the first frame to thesecond frame is unnatural (e.g., does not satisfy the motion model).

If the movement of the underlying structure is natural, then a positionof the virtual structure may be calculated for a third frame, andcontinues on in the manner described above. On the other hand, if themovement of the virtual structure is unnatural, then method the motionmodel may be used to provide a more reasonable estimate.

For example, assume that after a first pass, the location of a mark iswrong because it is off by one mark such that at the end the skeletondoes not fit the motion model very well, or fits well in one region butnot in another. In this case, the process is repeated for the regionthat does not fit the motion model very well.

Further, once a sequence has been reconstructed, the motion model canalso be used to clean up errors. By going backward and forward in time,the frames can be viewed as a sequence to verify that the individualaccelerations make sense as a sequence. For example, jittering and otherless noticeable errors can be addressed.

Referring again to FIG. 1, as noted above, any number of marks 112 canbe fully seen in a camera view of a support structure 110. The advantageof seeing more than one mark 112 in a camera view of a support structure110 is that there is an increased likelihood that the camera will have aclear view of at least one of the marks 112. However, if too many marks112 can be seen in a camera view (e.g., more than four marks 112 visiblein a camera view), then tracking errors may be more difficult toidentify.

For example, assume that a camera can see a single mark 112, and thesingle mark 112 is known to be attached to a leg. When the single mark112 is tracked from one frame to the next, if the leg mark 112 in acurrent frame is mistakenly identified in the next frame, the leg willincorrectly turn by almost by 90°, an easy mistake to identify.

On the other hand, if five or six marks 112 can be seen in a view andthe leg mark 112 in a current frame is mistakenly identified in the nextframe, then rather than being off by nearly 90° as is the case with asingle mark 112, a much smaller error is present. As a result, thenumber of marks 112 that can be seen in a view preferably provides adegree of exposure of each support structure 110 so that a mismatch inthe tracking would be easily exposed during reconstruction.

For example, two-to-three marks 112 per camera view of a supportstructure 110 increases the likelihood of clearly seeing one of themarks 112, along with the property that it is possible to be confusedwhen tracking marks 112 from one frame to the next, but when confusionis present (the wrong mark 112 is a second frame is identified as thecorresponding mark 112 from the first frame), the error is quitenoticeable and can be easily detected post process.

The support structure 110 placed around the waist may differ from theother support structures in that many more than three marks 112 can beseen from any camera position. The waist, however, is a unique areawhich is unlikely to be confused with something else. There is only onewaist, and the support structure 110 formed around the waist is also amuch different size, unlike the arms and legs which are somewhat similarin size.

In some embodiments, only one camera is needed to record images of aperson's motion at high speed while the positions of the marks 112 onthe support structures 110 in each image are tracked. When less thanthree marks on a support structure are visible in a frame, the 3Dposition of the support structure 110 can be estimated, which in turndrives an estimate of where the underlying skeleton must be.

Even when no individual support structure 110 can be estimatedprecisely, there are usually enough observations to uniquely recover theunderlying skeleton to which they are all attached. When the skeletoncannot be uniquely determined, statistics can be used on the person'smotion (derived from motion studies) to choose the most likely positionfor the under constrained portions.

The described embodiments may provide resistance to occlusion with afraction of the cameras (e.g., 20) required by a conventional motioncapture system, which may have two cameras tracking every dot during aframe. The reduction in cameras may reduce the costs of collecting andprocessing the motion data.

For example, assume that two cameras are used, and a second actor walksin front of a first camera, completely blocking the first camera's viewof the first actor. Using the system 100, the 3D position of a supportstructure 110 can be calculated based on the data captured from thesecond camera. Further, the system 100 may not need to see every mark112 all of the time. Using a substantially fixed spacing relationship,when the position of one or more marks 112 is determined, the positionof the support structure 110 can be determined or estimated.

The method may use fewer cameras than traditional techniques, and needonly use one. Each camera view can add more information about the entirestructure. Thus, if a support structure 110 is treated as a unifiedobject, and it can be seen from one view, then an estimate of itsposition can be made.

In some implementations, the system 100 works under a variety oflighting conditions, which may include complete darkness. In particular,it may allow high-fidelity motion to be captured outdoors or in brightlighting as is typically found on a film set during principal filmphotography.

FIG. 5 is a block diagram of a general computer system 500. The computersystem 500 can be used in the operations described above, according toone embodiment. For example, the system 500 may be included in any orall of the computer 122 and the storage medium 122.

The system 500 includes a processor 510, a memory 520, a storage device530 and an input/output device 540. Each of the components 510, 520, 530and 540 are interconnected using a system bus 550. The processor 510 iscapable of processing instructions for execution within the system 500.In one embodiment, the processor 510 is a single-threaded processor. Inanother embodiment, the processor 510 is a multi-threaded processor. Theprocessor 510 is capable of processing instructions stored in the memory520 or on the storage device 530 to display graphical information for auser interface on the input/output device 540.

The memory 520 stores information within the system 500. In oneembodiment, the memory 520 is a computer-readable medium. In oneembodiment, the memory 520 is a volatile memory unit. In anotherembodiment, the memory 520 is a non-volatile memory unit.

The storage device 530 is capable of providing mass storage for thesystem 500. In one embodiment, the storage device 530 is acomputer-readable medium. In various different embodiments, the storagedevice 530 may be a floppy disk device, a hard disk device, an opticaldisk device, or a tape device.

The input/output device 540 provides input/output operations for thesystem 500. In one embodiment, the input/output device 540 includes akeyboard and/or pointing device.

Various embodiments can be implemented in digital electronic circuitry,or in computer hardware, firmware, software, or in combinations of them.Apparatus can be implemented in a computer program product tangiblyembodied in an information carrier, e.g., in a machine-readable storagedevice or in a propagated signal, for execution by a programmableprocessor; and method steps of the various embodiments can be performedby a programmable processor executing a program of instructions toperform functions of the various embodiments by operating on input dataand generating output. The embodiments can be implemented advantageouslyin one or more computer programs that are executable on a programmablesystem including at least one programmable processor coupled to receivedata and instructions from, and to transmit data and instructions to, adata storage system, at least one input device, and at least one outputdevice. A computer program is a set of instructions that can be used,directly or indirectly, in a computer to perform a certain activity orbring about a certain result. A computer program can be written in anyform of programming language, including compiled or interpretedlanguages, and it can be deployed in any form, including as astand-alone program or as a module, component, subroutine, or other unitsuitable for use in a computing environment.

Suitable processors for the execution of a program of instructionsinclude, by way of example, both general and special purposemicroprocessors, and the sole processor or one of multiple processors ofany kind of computer. Generally, a processor will receive instructionsand data from a read-only memory or a random access memory or both. Theessential elements of a computer are a processor for executinginstructions and one or more memories for storing instructions and data.Generally, a computer will also include, or be operatively coupled tocommunicate with, one or more mass storage devices for storing datafiles; such devices include magnetic disks, such as internal hard disksand removable disks; magneto-optical disks; and optical disks. Storagedevices suitable for tangibly embodying computer program instructionsand data include all forms of non-volatile memory, including by way ofexample semiconductor memory devices, such as EPROM, EEPROM, and flashmemory devices; magnetic disks such as internal hard disks and removabledisks; magneto-optical disks; and CD-ROM and DVD-ROM disks. Theprocessor and the memory can be supplemented by, or incorporated in,ASICs (application-specific integrated circuits).

To provide for interaction with a user, various embodiments can beimplemented on a computer having a display device such as a CRT (cathoderay tube) or LCD (liquid crystal display) monitor for displayinginformation to the user and a keyboard and a pointing device such as amouse or a trackball by which the user can provide input to thecomputer.

The various embodiments can be implemented in a computer system thatincludes a back-end component, such as a data server, or that includes amiddleware component, such as an application server, such as the firstand second systems 102, 104, or an Internet server, or that includes afront-end component, such as a client computer having the UI 200 or anInternet browser, such as the browser 306, or any combination of them.The components of the system can be connected by any form or medium ofdigital data communication such as a communication network. Examples ofcommunication networks include, e.g., a LAN, a WAN, and the computersand networks forming the Internet.

The computer system can include clients and servers. A client and serverare generally remote from each other and typically interact through anetwork, such as the described one. The relationship of client andserver arises by virtue of computer programs running on the respectivecomputers and having a client-server relationship to each other.

A number of embodiments have been described. Nevertheless, it will beunderstood that various modifications may be made without departing fromthe spirit and scope of the described embodiments. For example, thesupport structures do not need to be attached over a body suit, but maybe directly fitted to an actor or other underlying object. In someembodiments, the marks also can be attached directly to the underlyingobject, for example, when the underlying object is a body suit worn bythe actor.

Also, the methods 200 and 300 are for illustrative purposes. The markson a particular support structure do not necessarily need to beprocessed before moving to a different support structure. The marks maybe processed in an order which processes marks irrespective of thesupport structure to which the marks belong. Similarly, marks capturedby different cameras may be processed in an order not dependent upon aparticular camera view. For example, a first mark from a first cameraview may be processed and then a second mark from a second camera viewmay be processed.

Additionally, the methods illustrated in FIGS. 2-4 may be performedsequentially, in parallel or in an order other than that which isdescribed. It should be appreciated that not all of the techniquesdescribed are required to be performed, that additional techniques maybe added, and that some of the illustrated techniques may be substitutedwith other techniques.

Also, in some embodiments, additional processing may occur when the raytraces are aligned with the known configuration of marks. For example, asmoothing process may be used in step 210. The smoothing calculation maycalculate a second derivative of the curve of a joint angle. The secondderivative may be included in error calculations used to determine thealignment of the marks with the ray traces. If the curve is noisy, thesecond derivative will be large. If the curve is smooth, the secondderivative will be small. The alignment may fit the data using thesecond derivative to select a position estimate for the virtualstructure that results in a smoother curve.

In yet other embodiments, the virtual structures can include geometricalaxes. For example, if an object without bones is captured, the virtualstructure may consist of axes within the object space occupied by theobject.

Additionally, in some embodiments, the object to be captured is not ahuman actor, but is another animal, such as a dog, a fish, a bird, orsnake. Accordingly, other embodiments are within the scope of thefollowing claims.

1. An apparatus comprising: a support structure including a band havinga substantially cylindrical shape, the band having a plurality of motioncapture marks each of which is positioned in a respective region of theband that visually contrasts with a corresponding motion capture mark,the support structure configured to be affixed to an object whose motionis to be tracked using one or more cameras, and the band being ofsufficient rigidity such that, in response to movement by the object,each motion capture mark from among the plurality of motion capturemarks on the band maintains substantially fixed distances from everyother motion capture mark.
 2. A motion capture system comprising: atleast one support structure including a band having a substantiallycylindrical shape, the band having a plurality of motion capture markseach of which is positioned in a respective region of the band thatvisually contrasts with a corresponding motion capture mark, the supportstructure configured to be affixed to an object whose motion is to betracked using one or more cameras, and the band being of sufficientrigidity such that, in response to movement by the object, each motioncapture mark from among the plurality of motion capture marks on theband maintains substantially fixed distances from every other motioncapture mark; and a computer system including a machine-readable storagedevice that stores instructions operable to cause the computer system toperform operations comprising: associating a position of the supportstructure to a position of a corresponding virtual support structure fora virtual skeleton, wherein one or more motion capture marks on the bandof the support structure substantially correspond in position to one ormore virtual marks on the virtual support structure; receiving a firstframe including one or more camera views of the support structure andselecting from the first frame one or more first motion capture marks onthe band of the support structure; and determining a first orientationof the virtual skeleton by substantially aligning one or more of thevirtual marks with one or more of the first motion capture marks asconstrained by one or more joints and movement possibilities of thevirtual skeleton, wherein one or more additional aligned virtual marksserves as an additional constraint for determining the firstorientation.
 3. The apparatus of claim 1, wherein the plurality ofmotion capture marks are positioned in alternating regions of the bandthat alternate between black and white, or between different colors. 4.The apparatus of claim 3, wherein an alternating region of the band hasa single mark.
 5. The apparatus of claim 1 wherein the motion capturemarks comprise one or more shapes.
 6. The apparatus of claim 1 whereinthe motion capture marks are evenly spaced apart.
 7. The apparatus ofclaim 1 wherein the object is a human and the band of the supportstructure is configured to be affixed around a torso, an arm or a leg ofthe human.
 8. The apparatus of claim 1 wherein one or more of the motioncapture marks has a uniquely identifiable characteristic.
 9. The systemof claim 2, wherein the plurality of motion capture marks are positionedin alternating regions of the band that alternate between black andwhite, or between different colors.
 10. The system of claim 9, whereinan alternating region of the band has a single motion capture mark. 11.The system of claim 2 wherein the motion capture marks comprise one ormore shapes.
 12. The system of claim 2 wherein the motion capture marksare evenly spaced apart.
 13. The system of claim 2 wherein the object isa human and the band of the support structure is configured to beaffixed around a torso, an arm or a leg of the human.
 14. The system ofclaim 2 wherein one or more of the motion capture marks has a uniquelyidentifiable characteristic.
 15. The system of claim 2 wherein thevirtual skeleton includes virtual bones connected by the joints, thesystem further comprising: a motion model that specifies a range ofmotion for one or more virtual bones with respect to one or more of thejoints, the motion model configured to determine the movementpossibilities of the virtual skeleton.
 16. The apparatus of claim 1,wherein the support structure is further configured such that whenattached to the object, fewer than all of the plurality of motioncapture marks on the band are visible within a field of view of any oneof the one or more cameras at any given time.
 17. The apparatus of claim1, wherein the support structure is further configured to be attached tothe object to substantially prevent translations and rotations relativeto the object of the plurality of motion capture marks on the band. 18.The system of claim 2, wherein the operations further comprisedetermining a three-dimensional position and an orientation of thesupport structure based on three or more observations of at least one ofthe plurality of motion capture marks on the band of the supportstructure, with the observations distributed among the one or morecamera views.
 19. The system of claim 2, wherein the operations furthercomprise determining a pose of at least part of the virtual skeletonbased on one or more observations of the at least one support structure,with the observations distributed among the one or more camera views.20. The system of claim 19, wherein one or two observations of the atleast one support structure is used for said determining the pose of theat least part of the skeleton.